Top Techniques to Analyze Data for Accurate and Actionable Insights

It’s a Monday morning, and your boss has a new task for you. She wants an in-depth analysis of the top-selling products for your company and their ideal price points for next quarter.

That’s not information you can easily extract from a look at recent sales. You might be better off using analytics methods to sort the data and estimate the best prices. There are several analytics techniques that can give you the results you’re looking for—and impress your boss!

In this article, we’ll explore data analysis methodologies and how to apply them.

Any time you make a decision, you rely on some form of data. The data can be internal—based on previous experiences or knowledge—or taken from external sources. Data analysis is the process of gathering data, refining it and using it to make an informed decision.

There are several steps involved in the data analysis process:

• Collection: Retrieving data from various sources
• Cleaning: Removing any inaccurate or irrelevant data points 
• Analysis: Reviewing data for key commonalities and trends
• Interpretation: Turning the data into a visual presentation, such as a graph
  

The results from data analysis help businesses (and individuals) make evidence-based decisions.

Data analysts use several basic analysis methods to clean, sort and review data.

This method involves finding several measures of central tendency in a data set, such as mean, median and mode.¹ It’s one of the least complex ways to analyze data. The results help analysts better understand a data set and present results using bar or line charts.¹

As an example, let’s say you want to analyze the grades of 50 students enrolled in a course. Descriptive statistics could show you average grades and their variability.

This analysis technique relies on sample data to draw a conclusion or inference.² Since the analyst doesn’t consider every data point, it’s easier to work with if you have vast amounts of information to review.²

You might use inferential statistics to estimate a group’s characteristics, traits or behaviors.

Need to examine the relationship between two or more variables? Regression analysis might help. It uses existing data to predict future outcomes based on variable or input changes.³ It can also help you pinpoint the variables that have the largest influence on an outcome.

In clustering, an analyst will group data according to similarities.⁴ If you’re working with a small dataset, it’s possible to cluster it manually. Larger populations might require machine learning algorithms to identify patterns or trends.⁴

You’ll see clustering whenever you open a streaming app that sorts media by genre. Other examples of clustering include grouping consumers by personal traits or categorizing users of a software program.

One popular analysis technique in research, particularly for weather trends, is time series analysis.⁵ It tracks changes between variables (or data points) over time.⁵ It’s also a beneficial prediction or forecast tool, especially when there’s a lot of historical data to pull from.⁵

Time series analysis produces useful visualizations such as charts and graphs. The visualization helps analysts interpret and present findings.

The results from data analysis have more impact when you present them in a visual format such as a chart or graph. The right data presentation makes it very easy to see trends and patterns that might not be clear from just looking at numbers or results.⁶ You can incorporate lines, colors and patterns to draw eyes directly to results gleaned from data analysis.⁶

Larger data sets may require more advanced analytic methodologies. One example is predictive analytics, which relies on historical data to predict a future outcome.⁷ It combines multiple tools, including statistics, machine learning and artificial intelligence (AI) to arrive at a conclusion.⁷ You’ll find predictive analytics to be valuable in nearly any industry, from banking to healthcare.

Another more advanced analytics methodology is sentiment analysis. It’s useful for tracking changes in consumer perceptions and behavior.⁸ You might use it to analyze the results from customer surveys or reviews.⁸

Big data analytics is helpful to any analyst or company working with extensive amounts of data. As an example, a bank might use it to analyze all of its customers’ transactions for signs of fraud. To work with big data analytics, you might use a relational database to organize and store information and structured query language (SQL) to extract results.⁹

There are several useful software programs and platforms for analytics techniques and data visualization.

This spreadsheet tool includes numerous formulas that prove very helpful for data analysis. You can also install the Data Analysis ToolPak add-on for additional statistical functions. Excel has many graphing and chart options, which help interpret and present results.

Tableau is a business intelligence tool built exclusively for data analysis. Its fully customizable dashboards simplify the data visualization process.¹⁰ The platform can pull data from multiple sources, including cloud services, spreadsheets and in-house databases.¹⁰

R isn’t a software program or platform; it’s a programming language. Many data analysts learn R since it’s very useful for statistical analysis and data visualization.¹¹ You may use it for basic and advanced data analytics methods that incorporate machine learning algorithms.¹¹

Python is another programming language common in data analysis settings. It’s highly versatile: you can use it to build a website, create an app and work with data.

Data analytics is a fast-growing field with many opportunities. Learning data analysis methodologies and techniques can provide you with the skills to accurately analyze data and turn it into a clear presentation for informed decision-making.

William & Mary’s Online Master of Science in Business Analytics provides rigorous training and education in commonly used data analysis techniques. Our faculty includes renowned professors with information technology, telecommunications, and cybersecurity backgrounds. You’ll learn from the best, so you can take your newfound skills to any company.

To learn more about William & Mary’s Online MSBA, schedule a call with an admissions outreach advisor.